| #!/usr/bin/env python3 |
| |
| import unittest |
| import random |
| import time |
| import pickle |
| import warnings |
| from math import log, exp, pi, fsum, sin |
| from test import support |
| |
| from typing import Any, Dict, List, Callable, Generic, TypeVar, cast |
| |
| RT = TypeVar('RT', random.Random, random.SystemRandom) |
| |
| class TestBasicOps(unittest.TestCase, Generic[RT]): |
| # Superclass with tests common to all generators. |
| # Subclasses must arrange for self.gen to retrieve the Random instance |
| # to be tested. |
| |
| gen = None # type: RT # Either Random or SystemRandom |
| |
| def randomlist(self, n: int) -> List[float]: |
| """Helper function to make a list of random numbers""" |
| return [self.gen.random() for i in range(n)] |
| |
| def test_autoseed(self) -> None: |
| self.gen.seed() |
| state1 = self.gen.getstate() |
| time.sleep(0.1) |
| self.gen.seed() # diffent seeds at different times |
| state2 = self.gen.getstate() |
| self.assertNotEqual(state1, state2) |
| |
| def test_saverestore(self) -> None: |
| N = 1000 |
| self.gen.seed() |
| state = self.gen.getstate() |
| randseq = self.randomlist(N) |
| self.gen.setstate(state) # should regenerate the same sequence |
| self.assertEqual(randseq, self.randomlist(N)) |
| |
| def test_seedargs(self) -> None: |
| for arg in [None, 0, 0, 1, 1, -1, -1, 10**20, -(10**20), |
| 3.14, complex(1., 2.), 'a', tuple('abc')]: |
| self.gen.seed(arg) |
| for arg in [list(range(3)), {'one': 1}]: |
| self.assertRaises(TypeError, self.gen.seed, arg) |
| self.assertRaises(TypeError, self.gen.seed, 1, 2, 3, 4) |
| self.assertRaises(TypeError, type(self.gen), []) # type: ignore # mypy issue 1846 |
| |
| def test_choice(self) -> None: |
| choice = self.gen.choice |
| with self.assertRaises(IndexError): |
| choice([]) |
| self.assertEqual(choice([50]), 50) |
| self.assertIn(choice([25, 75]), [25, 75]) |
| |
| def test_sample(self) -> None: |
| # For the entire allowable range of 0 <= k <= N, validate that |
| # the sample is of the correct length and contains only unique items |
| N = 100 |
| population = range(N) |
| for k in range(N+1): |
| s = self.gen.sample(population, k) |
| self.assertEqual(len(s), k) |
| uniq = set(s) |
| self.assertEqual(len(uniq), k) |
| self.assertTrue(uniq <= set(population)) |
| self.assertEqual(self.gen.sample([], 0), []) # test edge case N==k==0 |
| |
| def test_sample_distribution(self) -> None: |
| # For the entire allowable range of 0 <= k <= N, validate that |
| # sample generates all possible permutations |
| n = 5 |
| pop = range(n) |
| trials = 10000 # large num prevents false negatives without slowing normal case |
| def factorial(n: int) -> int: |
| if n == 0: |
| return 1 |
| return n * factorial(n - 1) |
| for k in range(n): |
| expected = factorial(n) // factorial(n-k) |
| perms = {} # type: Dict[tuple, object] |
| for i in range(trials): |
| perms[tuple(self.gen.sample(pop, k))] = None |
| if len(perms) == expected: |
| break |
| else: |
| self.fail() |
| |
| def test_sample_inputs(self) -> None: |
| # SF bug #801342 -- population can be any iterable defining __len__() |
| self.gen.sample(set(range(20)), 2) |
| self.gen.sample(range(20), 2) |
| self.gen.sample(range(20), 2) |
| self.gen.sample(str('abcdefghijklmnopqrst'), 2) |
| self.gen.sample(tuple('abcdefghijklmnopqrst'), 2) |
| |
| def test_sample_on_dicts(self) -> None: |
| self.assertRaises(TypeError, self.gen.sample, dict.fromkeys('abcdef'), 2) |
| |
| def test_gauss(self) -> None: |
| # Ensure that the seed() method initializes all the hidden state. In |
| # particular, through 2.2.1 it failed to reset a piece of state used |
| # by (and only by) the .gauss() method. |
| |
| for seed in 1, 12, 123, 1234, 12345, 123456, 654321: |
| self.gen.seed(seed) |
| x1 = self.gen.random() |
| y1 = self.gen.gauss(0, 1) |
| |
| self.gen.seed(seed) |
| x2 = self.gen.random() |
| y2 = self.gen.gauss(0, 1) |
| |
| self.assertEqual(x1, x2) |
| self.assertEqual(y1, y2) |
| |
| def test_pickling(self) -> None: |
| state = pickle.dumps(self.gen) |
| origseq = [self.gen.random() for i in range(10)] |
| newgen = pickle.loads(state) |
| restoredseq = [newgen.random() for i in range(10)] |
| self.assertEqual(origseq, restoredseq) |
| |
| def test_bug_1727780(self) -> None: |
| # verify that version-2-pickles can be loaded |
| # fine, whether they are created on 32-bit or 64-bit |
| # platforms, and that version-3-pickles load fine. |
| files = [("randv2_32.pck", 780), |
| ("randv2_64.pck", 866), |
| ("randv3.pck", 343)] |
| for file, value in files: |
| f = open(support.findfile(file),"rb") |
| r = pickle.load(f) |
| f.close() |
| self.assertEqual(int(r.random()*1000), value) |
| |
| def test_bug_9025(self) -> None: |
| # Had problem with an uneven distribution in int(n*random()) |
| # Verify the fix by checking that distributions fall within expectations. |
| n = 100000 |
| randrange = self.gen.randrange |
| k = sum(randrange(6755399441055744) % 3 == 2 for i in range(n)) |
| self.assertTrue(0.30 < k/n and k/n < .37, (k/n)) |
| |
| class SystemRandom_TestBasicOps(TestBasicOps[random.SystemRandom]): |
| gen = random.SystemRandom() |
| |
| def test_autoseed(self) -> None: |
| # Doesn't need to do anything except not fail |
| self.gen.seed() |
| |
| def test_saverestore(self) -> None: |
| self.assertRaises(NotImplementedError, self.gen.getstate) |
| self.assertRaises(NotImplementedError, self.gen.setstate, None) |
| |
| def test_seedargs(self) -> None: |
| # Doesn't need to do anything except not fail |
| self.gen.seed(100) |
| |
| def test_gauss(self) -> None: |
| self.gen.gauss_next = None |
| self.gen.seed(100) |
| self.assertEqual(self.gen.gauss_next, None) |
| |
| def test_pickling(self) -> None: |
| self.assertRaises(NotImplementedError, pickle.dumps, self.gen) |
| |
| def test_53_bits_per_float(self) -> None: |
| # This should pass whenever a C double has 53 bit precision. |
| span = 2 ** 53 # type: int |
| cum = 0 |
| for i in range(100): |
| cum |= int(self.gen.random() * span) |
| self.assertEqual(cum, span-1) |
| |
| def test_bigrand(self) -> None: |
| # The randrange routine should build-up the required number of bits |
| # in stages so that all bit positions are active. |
| span = 2 ** 500 # type: int |
| cum = 0 |
| for i in range(100): |
| r = self.gen.randrange(span) |
| self.assertTrue(0 <= r < span) |
| cum |= r |
| self.assertEqual(cum, span-1) |
| |
| def test_bigrand_ranges(self) -> None: |
| for i in [40,80, 160, 200, 211, 250, 375, 512, 550]: |
| start = self.gen.randrange(2 ** i) |
| stop = self.gen.randrange(2 ** (i-2)) |
| if stop <= start: |
| return |
| self.assertTrue(start <= self.gen.randrange(start, stop) < stop) |
| |
| def test_rangelimits(self) -> None: |
| for start, stop in [(-2,0), (-(2**60)-2,-(2**60)), (2**60,2**60+2)]: |
| self.assertEqual(set(range(start,stop)), |
| set([self.gen.randrange(start,stop) for i in range(100)])) |
| |
| def test_genrandbits(self) -> None: |
| # Verify ranges |
| for k in range(1, 1000): |
| self.assertTrue(0 <= self.gen.getrandbits(k) < 2**k) |
| |
| # Verify all bits active |
| getbits = self.gen.getrandbits |
| for span in [1, 2, 3, 4, 31, 32, 32, 52, 53, 54, 119, 127, 128, 129]: |
| cum = 0 |
| for i in range(100): |
| cum |= getbits(span) |
| self.assertEqual(cum, 2**span-1) |
| |
| # Verify argument checking |
| self.assertRaises(TypeError, self.gen.getrandbits) |
| self.assertRaises(TypeError, self.gen.getrandbits, 1, 2) |
| self.assertRaises(ValueError, self.gen.getrandbits, 0) |
| self.assertRaises(ValueError, self.gen.getrandbits, -1) |
| self.assertRaises(TypeError, self.gen.getrandbits, 10.1) |
| |
| def test_randbelow_logic(self, _log: Callable[[float, float], float] = log, |
| int: Callable[[float], int] = int) -> None: |
| # check bitcount transition points: 2**i and 2**(i+1)-1 |
| # show that: k = int(1.001 + _log(n, 2)) |
| # is equal to or one greater than the number of bits in n |
| for i in range(1, 1000): |
| n = 1 << i # check an exact power of two |
| numbits = i+1 |
| k = int(1.00001 + _log(n, 2)) |
| self.assertEqual(k, numbits) |
| self.assertEqual(n, 2**(k-1)) |
| |
| n += n - 1 # check 1 below the next power of two |
| k = int(1.00001 + _log(n, 2)) |
| self.assertIn(k, [numbits, numbits+1]) |
| self.assertTrue(2**k > n > 2**(k-2)) |
| |
| n -= n >> 15 # check a little farther below the next power of two |
| k = int(1.00001 + _log(n, 2)) |
| self.assertEqual(k, numbits) # note the stronger assertion |
| self.assertTrue(2**k > n > 2**(k-1)) # note the stronger assertion |
| |
| |
| class MersenneTwister_TestBasicOps(TestBasicOps[random.Random]): |
| gen = random.Random() |
| |
| def test_guaranteed_stable(self) -> None: |
| # These sequences are guaranteed to stay the same across versions of python |
| self.gen.seed(3456147, version=1) |
| self.assertEqual([self.gen.random().hex() for i in range(4)], |
| ['0x1.ac362300d90d2p-1', '0x1.9d16f74365005p-1', |
| '0x1.1ebb4352e4c4dp-1', '0x1.1a7422abf9c11p-1']) |
| self.gen.seed("the quick brown fox", version=2) |
| self.assertEqual([self.gen.random().hex() for i in range(4)], |
| ['0x1.1239ddfb11b7cp-3', '0x1.b3cbb5c51b120p-4', |
| '0x1.8c4f55116b60fp-1', '0x1.63eb525174a27p-1']) |
| |
| def test_setstate_first_arg(self) -> None: |
| self.assertRaises(ValueError, self.gen.setstate, (1, None, None)) |
| |
| def test_setstate_middle_arg(self) -> None: |
| # Wrong type, s/b tuple |
| self.assertRaises(TypeError, self.gen.setstate, (2, None, None)) |
| # Wrong length, s/b 625 |
| self.assertRaises(ValueError, self.gen.setstate, (2, (1,2,3), None)) |
| # Wrong type, s/b tuple of 625 ints |
| self.assertRaises(TypeError, self.gen.setstate, (2, tuple(['a',]*625), None)) |
| # Last element s/b an int also |
| self.assertRaises(TypeError, self.gen.setstate, (2, cast(Any, (0,))*624+('a',), None)) |
| |
| def test_referenceImplementation(self) -> None: |
| # Compare the python implementation with results from the original |
| # code. Create 2000 53-bit precision random floats. Compare only |
| # the last ten entries to show that the independent implementations |
| # are tracking. Here is the main() function needed to create the |
| # list of expected random numbers: |
| # void main(void){ |
| # int i; |
| # unsigned long init[4]={61731, 24903, 614, 42143}, length=4; |
| # init_by_array(init, length); |
| # for (i=0; i<2000; i++) { |
| # printf("%.15f ", genrand_res53()); |
| # if (i%5==4) printf("\n"); |
| # } |
| # } |
| expected = [0.45839803073713259, |
| 0.86057815201978782, |
| 0.92848331726782152, |
| 0.35932681119782461, |
| 0.081823493762449573, |
| 0.14332226470169329, |
| 0.084297823823520024, |
| 0.53814864671831453, |
| 0.089215024911993401, |
| 0.78486196105372907] |
| |
| self.gen.seed(61731 + (24903<<32) + (614<<64) + (42143<<96)) |
| actual = self.randomlist(2000)[-10:] |
| for a, e in zip(actual, expected): |
| self.assertAlmostEqual(a,e,places=14) |
| |
| def test_strong_reference_implementation(self) -> None: |
| # Like test_referenceImplementation, but checks for exact bit-level |
| # equality. This should pass on any box where C double contains |
| # at least 53 bits of precision (the underlying algorithm suffers |
| # no rounding errors -- all results are exact). |
| from math import ldexp |
| |
| expected = [0x0eab3258d2231f, |
| 0x1b89db315277a5, |
| 0x1db622a5518016, |
| 0x0b7f9af0d575bf, |
| 0x029e4c4db82240, |
| 0x04961892f5d673, |
| 0x02b291598e4589, |
| 0x11388382c15694, |
| 0x02dad977c9e1fe, |
| 0x191d96d4d334c6] |
| self.gen.seed(61731 + (24903<<32) + (614<<64) + (42143<<96)) |
| actual = self.randomlist(2000)[-10:] |
| for a, e in zip(actual, expected): |
| self.assertEqual(int(ldexp(a, 53)), e) |
| |
| def test_long_seed(self) -> None: |
| # This is most interesting to run in debug mode, just to make sure |
| # nothing blows up. Under the covers, a dynamically resized array |
| # is allocated, consuming space proportional to the number of bits |
| # in the seed. Unfortunately, that's a quadratic-time algorithm, |
| # so don't make this horribly big. |
| seed = (1 << (10000 * 8)) - 1 # about 10K bytes |
| self.gen.seed(seed) |
| |
| def test_53_bits_per_float(self) -> None: |
| # This should pass whenever a C double has 53 bit precision. |
| span = 2 ** 53 # type: int |
| cum = 0 |
| for i in range(100): |
| cum |= int(self.gen.random() * span) |
| self.assertEqual(cum, span-1) |
| |
| def test_bigrand(self) -> None: |
| # The randrange routine should build-up the required number of bits |
| # in stages so that all bit positions are active. |
| span = 2 ** 500 # type: int |
| cum = 0 |
| for i in range(100): |
| r = self.gen.randrange(span) |
| self.assertTrue(0 <= r < span) |
| cum |= r |
| self.assertEqual(cum, span-1) |
| |
| def test_bigrand_ranges(self) -> None: |
| for i in [40,80, 160, 200, 211, 250, 375, 512, 550]: |
| start = self.gen.randrange(2 ** i) |
| stop = self.gen.randrange(2 ** (i-2)) |
| if stop <= start: |
| return |
| self.assertTrue(start <= self.gen.randrange(start, stop) < stop) |
| |
| def test_rangelimits(self) -> None: |
| for start, stop in [(-2,0), (-(2**60)-2,-(2**60)), (2**60,2**60+2)]: |
| self.assertEqual(set(range(start,stop)), |
| set([self.gen.randrange(start,stop) for i in range(100)])) |
| |
| def test_genrandbits(self) -> None: |
| # Verify cross-platform repeatability |
| self.gen.seed(1234567) |
| self.assertEqual(self.gen.getrandbits(100), |
| 97904845777343510404718956115) |
| # Verify ranges |
| for k in range(1, 1000): |
| self.assertTrue(0 <= self.gen.getrandbits(k) < 2**k) |
| |
| # Verify all bits active |
| getbits = self.gen.getrandbits |
| for span in [1, 2, 3, 4, 31, 32, 32, 52, 53, 54, 119, 127, 128, 129]: |
| cum = 0 |
| for i in range(100): |
| cum |= getbits(span) |
| self.assertEqual(cum, 2**span-1) |
| |
| # Verify argument checking |
| self.assertRaises(TypeError, self.gen.getrandbits) |
| self.assertRaises(TypeError, self.gen.getrandbits, 'a') |
| self.assertRaises(TypeError, self.gen.getrandbits, 1, 2) |
| self.assertRaises(ValueError, self.gen.getrandbits, 0) |
| self.assertRaises(ValueError, self.gen.getrandbits, -1) |
| |
| def test_randbelow_logic(self, |
| _log: Callable[[int, float], float] = log, |
| int: Callable[[float], int] = int) -> None: |
| # check bitcount transition points: 2**i and 2**(i+1)-1 |
| # show that: k = int(1.001 + _log(n, 2)) |
| # is equal to or one greater than the number of bits in n |
| for i in range(1, 1000): |
| n = 1 << i # check an exact power of two |
| numbits = i+1 |
| k = int(1.00001 + _log(n, 2)) |
| self.assertEqual(k, numbits) |
| self.assertEqual(n, 2**(k-1)) |
| |
| n += n - 1 # check 1 below the next power of two |
| k = int(1.00001 + _log(n, 2)) |
| self.assertIn(k, [numbits, numbits+1]) |
| self.assertTrue(2**k > n > 2**(k-2)) |
| |
| n -= n >> 15 # check a little farther below the next power of two |
| k = int(1.00001 + _log(n, 2)) |
| self.assertEqual(k, numbits) # note the stronger assertion |
| self.assertTrue(2**k > n > 2**(k-1)) # note the stronger assertion |
| |
| def test_randrange_bug_1590891(self) -> None: |
| start = 1000000000000 |
| stop = -100000000000000000000 |
| step = -200 |
| x = self.gen.randrange(start, stop, step) |
| self.assertTrue(stop < x <= start) |
| self.assertEqual((x+stop)%step, 0) |
| |
| def gamma(z: float, sqrt2pi: float = (2.0*pi)**0.5) -> float: |
| # Reflection to right half of complex plane |
| if z < 0.5: |
| return pi / sin(pi*z) / gamma(1.0-z) |
| # Lanczos approximation with g=7 |
| az = z + (7.0 - 0.5) |
| return az ** (z-0.5) / exp(az) * sqrt2pi * fsum([ |
| 0.9999999999995183, |
| 676.5203681218835 / z, |
| -1259.139216722289 / (z+1.0), |
| 771.3234287757674 / (z+2.0), |
| -176.6150291498386 / (z+3.0), |
| 12.50734324009056 / (z+4.0), |
| -0.1385710331296526 / (z+5.0), |
| 0.9934937113930748e-05 / (z+6.0), |
| 0.1659470187408462e-06 / (z+7.0), |
| ]) |
| |
| class TestDistributions(unittest.TestCase): |
| def test_zeroinputs(self) -> None: |
| # Verify that distributions can handle a series of zero inputs' |
| g = random.Random() |
| x = [g.random() for i in range(50)] + [0.0]*5 |
| def patch() -> None: |
| setattr(g, 'random', x[:].pop) |
| patch(); g.uniform(1.0,10.0) |
| patch(); g.paretovariate(1.0) |
| patch(); g.expovariate(1.0) |
| patch(); g.weibullvariate(1.0, 1.0) |
| patch(); g.normalvariate(0.0, 1.0) |
| patch(); g.gauss(0.0, 1.0) |
| patch(); g.lognormvariate(0.0, 1.0) |
| patch(); g.vonmisesvariate(0.0, 1.0) |
| patch(); g.gammavariate(0.01, 1.0) |
| patch(); g.gammavariate(1.0, 1.0) |
| patch(); g.gammavariate(200.0, 1.0) |
| patch(); g.betavariate(3.0, 3.0) |
| patch(); g.triangular(0.0, 1.0, 1.0/3.0) |
| |
| def test_avg_std(self) -> None: |
| # Use integration to test distribution average and standard deviation. |
| # Only works for distributions which do not consume variates in pairs |
| g = random.Random() |
| N = 5000 |
| x = [i/float(N) for i in range(1,N)] |
| variate = None # type: Any |
| for variate, args, mu, sigmasqrd in [ |
| (g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12), |
| (g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0), |
| (g.expovariate, (1.5,), 1/1.5, 1/1.5**2), |
| (g.paretovariate, (5.0,), 5.0/(5.0-1), |
| 5.0/((5.0-1)**2*(5.0-2))), |
| (g.weibullvariate, (1.0, 3.0), gamma(1+1/3.0), |
| gamma(1+2/3.0)-gamma(1+1/3.0)**2) ]: |
| setattr(g, 'random', x[:].pop) |
| y = [] # type: List[float] |
| for i in range(len(x)): |
| try: |
| y.append(variate(*args)) |
| except IndexError: |
| pass |
| s1 = s2 = 0.0 |
| for e in y: |
| s1 += e |
| s2 += (e - mu) ** 2 |
| N = len(y) |
| self.assertAlmostEqual(s1/N, mu, places=2) |
| self.assertAlmostEqual(s2/(N-1), sigmasqrd, places=2) |
| |
| class TestModule(unittest.TestCase): |
| def testMagicConstants(self) -> None: |
| self.assertAlmostEqual(random.NV_MAGICCONST, 1.71552776992141) |
| self.assertAlmostEqual(random.TWOPI, 6.28318530718) |
| self.assertAlmostEqual(random.LOG4, 1.38629436111989) |
| self.assertAlmostEqual(random.SG_MAGICCONST, 2.50407739677627) |
| |
| def test__all__(self) -> None: |
| # tests validity but not completeness of the __all__ list |
| self.assertTrue(set(random.__all__) <= set(dir(random))) |
| |
| def test_random_subclass_with_kwargs(self) -> None: |
| # SF bug #1486663 -- this used to erroneously raise a TypeError |
| class Subclass(random.Random): |
| def __init__(self, newarg: object = None) -> None: |
| random.Random.__init__(self) |
| Subclass(newarg=1) |
| |
| |
| def test_main(verbose: bool = None) -> None: |
| testclasses = [MersenneTwister_TestBasicOps, |
| TestDistributions, |
| TestModule] |
| |
| try: |
| random.SystemRandom().random() |
| except NotImplementedError: |
| pass |
| else: |
| testclasses.append(SystemRandom_TestBasicOps) |
| |
| support.run_unittest(*testclasses) |
| |
| # verify reference counting |
| import sys |
| if verbose and hasattr(sys, "gettotalrefcount"): |
| counts = [None] * 5 # type: List[int] |
| for i in range(len(counts)): |
| support.run_unittest(*testclasses) |
| counts[i] = sys.gettotalrefcount() |
| print(counts) |
| |
| if __name__ == "__main__": |
| test_main(verbose=True) |
